# SCALEX: Single-cell integrative Analysis via latent Feature EXtraction
## Installation #### install from PyPI
pip install scalex
#### install from GitHub
git clone git://github.com/jsxlei/scalex.git cd scalex python setup.py install
scalex is implemented in [Pytorch](https://pytorch.org/) framework. Running scalex on CUDA is recommended if available. Installation only requires a few minutes.
## Quick Start
### 1. Command line
SCALEX.py –data_list data1 data2 –batch_categories batch1 batch2
data_list: data path of each batch of single-cell dataset batch_categories: name of each batch
#### Output Output will be saved in the output folder including: * checkpoint: saved model to reproduce results cooperated with option –checkpoint or -c * adata.h5ad: preprocessed data and results including, latent, clustering and imputation * umap.png: UMAP visualization of latent representations of cells * log.txt: log file of training process
#### Useful options * output folder for saveing results: [-o] or [–outdir] * filter rare genes, default 3: [–min_cell] * filter low quality cells, default 600: [–min_gene] * select the number of highly variable genes, keep all genes with -1, default 2000: [–n_top_genes]
#### Help Look for more usage of scalex
SCALEX.py –help
### 2. API function
from scalex.function import SCALEX adata = SCALEX(data_list, batch_categories)
Function of parameters are similar to command line options. Output is a Anndata object for further analysis with scanpy.
#### Tutorial
1 maintainer
Release files for scalex 0.0.8
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| scalex-0.0.8-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 75.1 kB
Release files / scalex-0.0.8.tar.gz
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